scholarly journals FINDING A GOOD FEATURE DETECTOR-DESCRIPTOR COMBINATION FOR THE 2D KEYPOINT-BASED REGISTRATION OF TLS POINT CLOUDS

2015 ◽  
Vol II-3/W5 ◽  
pp. 121-128 ◽  
Author(s):  
S. Urban ◽  
M. Weinmann
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Scale Invariant ◽  
Point Cloud Registration ◽  
Speeded Up Robust Features ◽  
Extensive Evaluation ◽  
Feature Correspondences ◽  
Intensity Images ◽  
Keypoint Detector

The automatic and accurate registration of terrestrial laser scanning (TLS) data is a topic of great interest in the domains of city modeling, construction surveying or cultural heritage. While numerous of the most recent approaches focus on keypoint-based point cloud registration relying on forward-projected 2D keypoints detected in panoramic intensity images, little attention has been paid to the selection of appropriate keypoint detector-descriptor combinations. Instead, keypoints are commonly detected and described by applying well-known methods such as the Scale Invariant Feature Transform (SIFT) or Speeded-Up Robust Features (SURF). In this paper, we present a framework for evaluating the influence of different keypoint detector-descriptor combinations on the results of point cloud registration. For this purpose, we involve five different approaches for extracting local features from the panoramic intensity images and exploit the range information of putative feature correspondences in order to define bearing vectors which, in turn, may be exploited to transfer the task of point cloud registration from the object space to the observation space. With an extensive evaluation of our framework on a standard benchmark TLS dataset, we clearly demonstrate that replacing SIFT and SURF detectors and descriptors by more recent approaches significantly alleviates point cloud registration in terms of accuracy, efficiency and robustness.

scholarly journals PERSPECTIVE INTENSITY IMAGES FOR CO-REGISTRATION OF TERRESTRIAL LASER SCANNER AND DIGITAL CAMERA

2016 ◽  
Vol XLI-B3 ◽  
pp. 295-300
Author(s):  
Yubin Liang ◽  
Yan Qiu ◽  
Tiejun Cui
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Least Squares Method ◽  
Laser Scanner ◽  
Digital Camera ◽  
Point Clouds ◽  
Terrestrial Laser Scanner ◽  
Automatic Registration ◽  
Intensity Images ◽  
Intensity Image

Co-registration of terrestrial laser scanner and digital camera has been an important topic of research, since reconstruction of visually appealing and measurable models of the scanned objects can be achieved by using both point clouds and digital images. This paper presents an approach for co-registration of terrestrial laser scanner and digital camera. A perspective intensity image of the point cloud is firstly generated by using the collinearity equation. Then corner points are extracted from the generated perspective intensity image and the camera image. The fundamental matrix F is then estimated using several interactively selected tie points and used to obtain more matches with RANSAC. The 3D coordinates of all the matched tie points are directly obtained or estimated using the least squares method. The robustness and effectiveness of the presented methodology is demonstrated by the experimental results. Methods presented in this work may also be used for automatic registration of terrestrial laser scanning point clouds.

scholarly journals TLS POINT CLOUD REGISTRATION FOR DETECTING CHANGE IN INDIVIDUAL ROCKS OF A MOUNTAIN RIVER BED

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1149-1154
Author(s):  
A. Walicka ◽  
N. Pfeifer ◽  
G. Jóźków ◽  
A. Borkowski
Keyword(s):  
Gaussian Curvature ◽  
Point Cloud ◽  
Laser Scanning ◽  
Nearest Neighbor ◽  
Point Clouds ◽  
Point Cloud Registration ◽  
River Bed ◽  
Key Points ◽  
Transformation Parameters ◽  
Movement Parameters

<p><strong>Abstract.</strong> Remote sensing techniques are an important tool in fluvial transport monitoring, since they allow for effective evaluation of the volume of transported material. Nevertheless, there is no methodology for automatic calculation of movement parameters of individual rocks. These parameters can be determined by point cloud registration. Hence, the goal of this study is to develop a robust algorithm for terrestrial laser scanning point cloud registration. The registration is based on Iterative Closest Point algorithm, which requires well established initial parameters of transformation. Thus, we propose to calculate the initial parameters based on key points representing the maximum of Gaussian curvature. For each key point the set of geometrical features is calculated. The key points are then matched between two point clouds as a nearest neighbor in feature domain. Different combinations of neighborhood sizes, feature subsets, metrics and number of nearest neighbors were tested to obtain the highest ratio between properly and improperly matched key points. Finally, RANSAC algorithm was used to calculate the initial transformation parameters between the point clouds and the ICP algorithm was used for calculation of final transformation parameters. The investigations carried out on sample point clouds representing rocks enabled the adjustment of parameters of the algorithm and showed that the Gaussian curvature can be used as a 3-dimentional key point detector for such objects. The proposed algorithm enabled to register point clouds with the mean distance between point clouds equal to 3&amp;thinsp;mm.</p>

scholarly journals PERSPECTIVE INTENSITY IMAGES FOR CO-REGISTRATION OF TERRESTRIAL LASER SCANNER AND DIGITAL CAMERA

2016 ◽  
Vol XLI-B3 ◽  
pp. 295-300
Author(s):  
Yubin Liang ◽  
Yan Qiu ◽  
Tiejun Cui
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Least Squares Method ◽  
Laser Scanner ◽  
Digital Camera ◽  
Point Clouds ◽  
Terrestrial Laser Scanner ◽  
Automatic Registration ◽  
Intensity Images ◽  
Intensity Image

Co-registration of terrestrial laser scanner and digital camera has been an important topic of research, since reconstruction of visually appealing and measurable models of the scanned objects can be achieved by using both point clouds and digital images. This paper presents an approach for co-registration of terrestrial laser scanner and digital camera. A perspective intensity image of the point cloud is firstly generated by using the collinearity equation. Then corner points are extracted from the generated perspective intensity image and the camera image. The fundamental matrix F is then estimated using several interactively selected tie points and used to obtain more matches with RANSAC. The 3D coordinates of all the matched tie points are directly obtained or estimated using the least squares method. The robustness and effectiveness of the presented methodology is demonstrated by the experimental results. Methods presented in this work may also be used for automatic registration of terrestrial laser scanning point clouds.

scholarly journals The Influence of the Cartographic Transformation of TLS Data on the Quality of the Automatic Registration

2019 ◽  
Vol 9 (3) ◽  
pp. 509 ◽  
Author(s):  
Jakub Markiewicz ◽  
Dorota Zawieska
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Bundle Adjustment ◽  
Quality Analysis ◽  
Automatic Registration ◽  
Speeded Up Robust Features ◽  
Check Points ◽  
Characteristic Points ◽  
Selection Of

This paper discusses the issue of the influence of cartographic Terrestrial Laser Scanning (TLS) data conversion into feature-based automatic registration. Automatic registration of data is a multi-stage process, it is based on original software tools and consists of: (1) Conversion of data to the raster form, (2) register of TLS data in pairs in all possible combinations using the SURF (Speeded Up Robust Features) and FAST (Features from Accelerated Segment Test) algorithms, (3) the quality analysis of relative orientation of processed pairs, and (4) the final bundle adjustment. The following two problems, related to the influence of the spherical image, the orthoimage and the Mercator representation of the point cloud, are discussed: The correctness of the automatic tie points detection and distribution and the influence of the TLS position on the completeness of the registration process and the quality assessment. The majority of popular software applications use manually or semi-automatically determined corresponding points. However, the authors propose an original software tool to address the first issue, which automatically detects and matches corresponding points on each TLS raster representation, utilizing different algorithms (SURF and FAST). To address the second task, the authors present a series of analyses: The time of detection of characteristic points, the percentage of incorrectly detected points and adjusted characteristic points, the number of detected control and check points, the orientation accuracy of control and check points, and the distribution of control and check points. Selection of an appropriate method for the TLS point cloud conversion to the raster form and selection of an appropriate algorithm, considerably influence the completeness of the entire process, and the accuracy of data orientation. The results of the performed experiments show that fully automatic registration of the TLS point clouds in the raster forms is possible; however, it is not possible to propose one, universal form of the point cloud, because a priori knowledge concerning the scanner positions is required. If scanner stations are located close to one another in raster images or in spherical images, Mercator projections are recommended. In the case where fragments of the surface are measured under different angles from different distances and heights of the TLS, orthoimages are suggested.

scholarly journals COMPARING THE PERFORMANCE OF POINT CLOUD REGISTRATION METHODS FOR LANDSLIDE MONITORING USING MOBILE LASER SCANNING DATA

2018 ◽  
Vol XLII-4/W9 ◽  
pp. 11-21 ◽  
Author(s):  
N. Ahmad Fuad ◽  
A. R. Yusoff ◽  
Z. Ismail ◽  
Z. Majid
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Least Square ◽  
Landslide Monitoring ◽  
Scanning System ◽  
Point Cloud Registration ◽  
Distance Computation ◽  
3D Surface ◽  
Surface Deviation

<p><strong>Abstract.</strong> The aim of the research is to evaluate the performance of the point cloud registration methods using mobile laser scanning data. The point cloud registration methods involved in this research are match bounding-box centres and iterative closest point (ICP). The research began with the two epoch’s mobile laser scanning survey using a Phoenix AL-3-32 system. At the same time, the stereo images of the study area were acquired using UAV Photogrammetric method. Both two epoch point cloud datasets were gone through the pre and post-processing stages to produce the cleaned and geo-referenced point clouds data. The data were then gone through the two registration methods and four Cloud-to-Cloud (C2C) distance methods. The 3D surface deviation results obtained from mobile laser scanning data was compared with the 3D surface deviation results from UAV data that undergoes the same registration and C2C distance computation methods. The study area involved in the research is an active landslide area that was located at Kulim Hi-Tech residential area in Kedah state, Malaysia. The study area exposed to the movement of the land which caused cracked to the buildings and drainages. The findings show that the ICP registration becomes the most suitable method to register point clouds dataset that was acquired using mobile laser scanning system. Among the four C2C distance computation methods that was involved in the testing, the least square plane method was the best method to calculate the distance between two sets of point clouds datasets which in turn gave the best results in the process of detecting the movement of the land in the study area.</p>

scholarly journals THE COMPARISON OF 2D AND 3D DETECTORS FOR TLS DATA REGISTRATION &ndash; PRELIMINARY RESULTS

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 467-472 ◽  
Author(s):  
J. S. Markiewicz ◽  
Ł. Markiewicz ◽  
P. Foryś
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Scale Invariant ◽  
Data Registration ◽  
Registration Process ◽  
3D Data ◽  
Panoramic Images ◽  
Scale Invariant Feature ◽  
2D And 3D

<p><strong>Abstract.</strong> This paper presents the analysis of possible methods of a terrestrial laser scanning (TLS) data registration using 2D/3D detectors and descriptors. The developed approach, where point clouds are processed in form of panoramic images, orthoimages and 3D data, was described. The accuracy of the registration process was preliminary verified. The two approaches were analysed and compared: the 2D SIFT (Scale-Invariant Feature Transform) detector and descriptor with the rasterized TLS data and the 3D SIFT detector with the 3D FPFH (Fast Point Feature Histograms) descriptor. The feature points were found and preliminary matched using the OpenCV and PCL (Point Cloud Library) libraries. In order to find the best point cloud representation for the registration process, both the percentage and distribution of the correctly detected and matched points were analysed. The materials consisted of the point clouds of two chambers from the Museum of King John III’s Palace in Wilanów. They were acquired using the Z+F 5006h and 5003 TLS scanners. The performed analysis showed that the lowest RMSE values were for the 2D detectors and orthoimages. However, in the case of the point number and distribution, better results were obtained for using the 3D detector.</p>

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

scholarly journals Revealing Changes in the Stem Form and Volume Allocation in Diverse Boreal Forests Using Two-Date Terrestrial Laser Scanning

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 835
Author(s):  
Ville Luoma ◽  
Tuomas Yrttimaa ◽  
Ville Kankare ◽  
Ninni Saarinen ◽  
Jiri Pyörälä ◽  
...  
Keyword(s):  
Tree Growth ◽  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Stem Volume ◽  
Accurate Information ◽  
Stem Form ◽  
Monitoring Period ◽  
Significant Difference

Tree growth is a multidimensional process that is affected by several factors. There is a continuous demand for improved information on tree growth and the ecological traits controlling it. This study aims at providing new approaches to improve ecological understanding of tree growth by the means of terrestrial laser scanning (TLS). Changes in tree stem form and stem volume allocation were investigated during a five-year monitoring period. In total, a selection of attributes from 736 trees from 37 sample plots representing different forest structures were extracted from taper curves derived from two-date TLS point clouds. The results of this study showed the capability of point cloud-based methods in detecting changes in the stem form and volume allocation. In addition, the results showed a significant difference between different forest structures in how relative stem volume and logwood volume increased during the monitoring period. Along with contributing to providing more accurate information for monitoring purposes in general, the findings of this study showed the ability and many possibilities of point cloud-based method to characterize changes in living organisms in particular, which further promote the feasibility of using point clouds as an observation method also in ecological studies.

scholarly journals Comparing High Accuracy t-LiDAR and UAV-SfM Derived Point Clouds for Geomorphological Change Detection

2021 ◽  
Vol 10 (6) ◽  
pp. 367
Author(s):  
Simoni Alexiou ◽  
Georgios Deligiannakis ◽  
Aggelos Pallikarakis ◽  
Ioannis Papanikolaou ◽  
Emmanouil Psomiadis ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Change Detection ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Mean Value ◽  
Detection Methods ◽  
Mountainous Catchments ◽  
Aerial Vehicle ◽  
Soil Erosion Assessment

Analysis of two small semi-mountainous catchments in central Evia island, Greece, highlights the advantages of Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanning (TLS) based change detection methods. We use point clouds derived by both methods in two sites (S1 & S2), to analyse the effects of a recent wildfire on soil erosion. Results indicate that topsoil’s movements in the order of a few centimetres, occurring within a few months, can be estimated. Erosion at S2 is precisely delineated by both methods, yielding a mean value of 1.5 cm within four months. At S1, UAV-derived point clouds’ comparison quantifies annual soil erosion more accurately, showing a maximum annual erosion rate of 48 cm. UAV-derived point clouds appear to be more accurate for channel erosion display and measurement, while the slope wash is more precisely estimated using TLS. Analysis of Point Cloud time series is a reliable and fast process for soil erosion assessment, especially in rapidly changing environments with difficult access for direct measurement methods. This study will contribute to proper georesource management by defining the best-suited methodology for soil erosion assessment after a wildfire in Mediterranean environments.

scholarly journals Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon

2021 ◽  
Vol 13 (2) ◽  
pp. 261
Author(s):  
Francisco Mauro ◽  
Andrew T. Hudak ◽  
Patrick A. Fekety ◽  
Bryce Frank ◽  
Hailemariam Temesgen ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Field Measurements ◽  
Point Clouds ◽  
Semiparametric Models ◽  
Airborne Laser Scanning ◽  
Parametric Models ◽  
Fuel Load ◽  
Forest Fuels ◽  
Airborne Laser

Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas. In this study, we analyze different factors that can contribute to developing a regional strategy to use information from completed ALS data acquisitions and develop maps of multiple forest attributes in new ALS project areas in a rapid manner. This study is located in Oregon, USA, and analyzes six forest structural attributes for differences between: (1) synthetic (i.e., not-calibrated), and calibrated predictions, (2) parametric linear and semiparametric models, and (3) models developed with predictors computed for point clouds enclosed in the areas where field measurements were taken, i.e., “point-cloud predictors”, and models developed using predictors extracted from pre-rasterized layers, i.e., “rasterized predictors”. Forest structural attributes under consideration are aboveground biomass, downed woody biomass, canopy bulk density, canopy height, canopy base height, and canopy fuel load. Results from our study indicate that semiparametric models perform better than parametric models if no calibration is performed. However, the effect of the calibration is substantial in reducing the bias of parametric models but minimal for the semiparametric models and, once calibrations are performed, differences between parametric and semiparametric models become negligible for all responses. In addition, minimal differences between models using point-cloud predictors and models using rasterized predictors were found. We conclude that the approach that applies semiparametric models and rasterized predictors, which represents the easiest workflow and leads to the most rapid results, is justified with little loss in accuracy or precision even if no calibration is performed.

Sign in / Sign up

Export Citation Format

Share Document